# Semi-supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game theoretic Perspective

arXiv: https://arxiv.org/abs/2108.01852 · HTML: https://mlsanity.com/paper/2108.01852/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2021.

## Built on by (most cited first, 3 of 3)

- [Web Content Filtering through knowledge distillation of Large Language Models](https://mlsanity.com/paper/2305.05027/) (2023)
- [From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories](https://mlsanity.com/paper/2504.16449/) (2025)
- [WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT](https://mlsanity.com/paper/2506.19356/) (2025)

## Abstract (arXiv)

Spear Phishing is a type of cyber-attack where the attacker sends hyperlinks through email on well-researched targets. The objective is to obtain sensitive information by imitating oneself as a trustworthy website. In recent times, deep learning has become the standard for defending against such attacks. However, these architectures were designed with only defense in mind. Moreover, the attacker's perspective and motivation are absent while creating such models. To address this, we need a game-theoretic approach to understand the perspective of the attacker (Hacker) and the defender (Phishing URL detector). We propose a Conditional Generative Adversarial Network with novel training strategy for real-time phishing URL detection. Additionally, we train our architecture in a semi-supervised manner to distinguish between adversarial and real examples, along with detecting malicious and benign URLs. We also design two games between the attacker and defender in training and deployment settings by utilizing the game-theoretic perspective. Our experiments confirm that the proposed architecture surpasses recent state-of-the-art architectures for phishing URLs detection.